Statistics for Corpus Linguists: From Theory to Practice
  • Overview
  • Datasets
  • Resources
    • R Cheatsheets
    • Further Reading
  1. 7. Multivariate data analysis
  2. 7.3 Clustering
  • Overview
  • 1. Theory
    • 1.1 Basics
    • 1.2 Research Questions
    • 1.3 Linguistic Variables
  • 2. Introduction to R
    • 2.1 Vectors I
    • 2.2 Data frames I
    • 2.3 Libraries
    • 2.4 Importing/Exporting
  • 3. NLP with R
    • 3.1 Concordancing
    • 3.2 Regex
    • 3.3 Data Annotation
  • 4. Descriptive Statistics
    • 4.1 Data types
    • 4.2 Categorical data
    • 4.3 Continuous data
  • 5. Inferential Statistics
    • 5.1 Hypothesis testing
    • 5.2 Chi-squared test
    • 5.3 t-test
  • 6. Statistical Modelling
    • 6.1 Linear regression
    • 6.2 Logistic regression
    • 6.3 Mixed-effects regression
    • 6.4 Poisson regression
    • 6.5 Ordinal regression
    • 6.6 Tree-based methods
    • 6.7 Gradient boosting
  • 7. Multivariate data analysis
    • 7.1 PCA
    • 7.2 EFA
    • 7.3 Clustering

On this page

  • 1 Recommended reading
  • 2 Preparation
  • 3 Clustering algorithms
    • 3.1 \(k\)-means
    • 3.2 Partitioning around medoids (PAM)
    • 3.3 Hierarchical agglomerative clustering
  1. 7. Multivariate data analysis
  2. 7.3 Clustering

7.3 Clustering

Author
Affiliation

Vladimir Buskin

Catholic University of Eichstätt-Ingolstadt

1 Recommended reading

James et al. (2021): Chapter 12

Hastie, Tibshirani, and Friedman (2017): Chapters 14.3.6, 14.3.10 & 14.3.12

2 Preparation

3 Clustering algorithms

Warning

This page is still under construction. More content will be added soon!

3.1 \(k\)-means

3.2 Partitioning around medoids (PAM)

Back to top

3.3 Hierarchical agglomerative clustering

Hastie, Trevor, Robert Tibshirani, and Jerome H. Friedman. 2017. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. New York, NY: Springer.
James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2021. An Introduction to Statistical Learning: With Applications in r. New York: Springer. https://doi.org/10.1007/978-1-0716-1418-1.
7.2 EFA